Materials Informatics: Overview

2019 ◽  
pp. 1-7
Author(s):  
Nicola Marzari
Keyword(s):  
Materials Informatics

Accelerated Design of Photovoltaic Dyes Using Materials Informatics

2019 ◽  
Author(s):  
Vishwesh Venkatraman ◽  
John de Mello
Keyword(s):  
Materials Informatics

scholarly journals Optimization of the composition in a composite material for microelectronics application using the Ising model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yoshihiko Imanaka ◽  
Toshihisa Anazawa ◽  
Fumiaki Kumasaka ◽  
Hideyuki Jippo
Keyword(s):  
Thermal Conductivity ◽  
Thermal Expansion ◽  
Ising Model ◽  
Specific Gravity ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Industrial Applications ◽  
Thermal Dissipation ◽  
Materials Informatics ◽  
Combinatorial Optimization Problems

AbstractTailored material is necessary in many industrial applications since material properties directly determine the characteristics of components. However, the conventional trial and error approach is costly and time-consuming. Therefore, materials informatics is expected to overcome these drawbacks. Here, we show a new materials informatics approach applying the Ising model for solving discrete combinatorial optimization problems. In this study, the composition of the composite, aimed at developing a heat sink with three necessary properties: high thermal dissipation, attachability to Si, and a low weight, is optimized. We formulate an energy function equation concerning three objective terms with regard to the thermal conductivity, thermal expansion and specific gravity, with the composition variable and two constrained terms with a quadratic unconstrained binary optimization style equivalent to the Ising model and calculated by a simulated annealing algorithm. The composite properties of the composition selected from ten constituents are verified by the empirical mixture rule of the composite. As a result, an optimized composition with high thermal conductivity, thermal expansion close to that of Si, and a low specific gravity is acquired.

scholarly journals Enabling deeper learning on big data for materials informatics applications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dipendra Jha ◽  
Vishu Gupta ◽  
Logan Ward ◽  
Zijiang Yang ◽  
Christopher Wolverton ◽  
...  
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Materials Science ◽  
Prediction Models ◽  
Model Performance ◽  
Materials Informatics ◽  
Learning Framework ◽  
Significant Attention

AbstractThe application of machine learning (ML) techniques in materials science has attracted significant attention in recent years, due to their impressive ability to efficiently extract data-driven linkages from various input materials representations to their output properties. While the application of traditional ML techniques has become quite ubiquitous, there have been limited applications of more advanced deep learning (DL) techniques, primarily because big materials datasets are relatively rare. Given the demonstrated potential and advantages of DL and the increasing availability of big materials datasets, it is attractive to go for deeper neural networks in a bid to boost model performance, but in reality, it leads to performance degradation due to the vanishing gradient problem. In this paper, we address the question of how to enable deeper learning for cases where big materials data is available. Here, we present a general deep learning framework based on Individual Residual learning (IRNet) composed of very deep neural networks that can work with any vector-based materials representation as input to build accurate property prediction models. We find that the proposed IRNet models can not only successfully alleviate the vanishing gradient problem and enable deeper learning, but also lead to significantly (up to 47%) better model accuracy as compared to plain deep neural networks and traditional ML techniques for a given input materials representation in the presence of big data.

Materials informatics part I: A diversity of issues

JOM ◽  
2008 ◽  
Vol 60 (3) ◽  
pp. 50-50 ◽  
Author(s):  
Krishna Rajan
Keyword(s):  
Materials Informatics

scholarly journals Design of peptides with strong binding affinity to poly(methyl methacrylate) resin by use of molecular simulation-based materials informatics

2021 ◽  
Author(s):  
Tomio Iwasaki ◽  
Masashi Maruyama ◽  
Tatsuya Niwa ◽  
Toshiki Sawada ◽  
Takeshi Serizawa
Keyword(s):  
Methyl Methacrylate ◽  
Binding Affinity ◽  
Molecular Simulation ◽  
Dynamics Simulation ◽  
Binding Affinities ◽  
Materials Informatics ◽  
Poly Methyl Methacrylate ◽  
Strong Binding ◽  
Experimental Values ◽  
Pmma Resin

AbstractPeptides with strong binding affinities for poly(methyl methacrylate) (PMMA) resin were designed by use of materials informatics technology based on molecular dynamics simulation for the purpose of covering the resin surface with adhesive peptides, which were expected to result in eco-friendly and biocompatible biomaterials. From the results of binding affinity obtained with this molecular simulation, it was confirmed that experimental values could be predicted with errors <10%. By analyzing the simulation data with the response-surface method, we found that three peptides (RWWRPWW, EWWRPWR, and RWWRPWR), which consist of arginine (R), tryptophan (W), and proline (P), have strong binding affinity to the PMMA resin. These amino acids were effective because arginine and tryptophan have strong binding affinities for methoxycarbonyl groups and methyl groups, which are the main constituents of the PMMA resin, and proline stabilizes the flat zigzag structures of the peptides in water. The strong binding affinities of the three peptides were confirmed by experiments (surface plasmon resonance methods).

Materials Informatics

2009 ◽  
Vol 1 (5) ◽  
pp. 286-286 ◽  
Author(s):  
Krishna Rajan ◽  
Patricio Mendez
Keyword(s):  
Materials Informatics

scholarly journals ChemProps: A RESTful API enabled database for composite polymer name standardization

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Bingyin Hu ◽  
Anqi Lin ◽  
L. Catherine Brinson
Keyword(s):  
Data Exchange ◽  
Ground Truth ◽  
Training Data ◽  
Test Accuracy ◽  
Polymer Science ◽  
Data Systems ◽  
Materials Informatics ◽  
Related Data ◽  
Weight Factors ◽  
Data Points

AbstractThe inconsistency of polymer indexing caused by the lack of uniformity in expression of polymer names is a major challenge for widespread use of polymer related data resources and limits broad application of materials informatics for innovation in broad classes of polymer science and polymeric based materials. The current solution of using a variety of different chemical identifiers has proven insufficient to address the challenge and is not intuitive for researchers. This work proposes a multi-algorithm-based mapping methodology entitled ChemProps that is optimized to solve the polymer indexing issue with easy-to-update design both in depth and in width. RESTful API is enabled for lightweight data exchange and easy integration across data systems. A weight factor is assigned to each algorithm to generate scores for candidate chemical names and optimized to maximize the minimum value of the score difference between the ground truth chemical name and the other candidate chemical names. Ten-fold validation is utilized on the 160 training data points to prevent overfitting issues. The obtained set of weight factors achieves a 100% test accuracy on the 54 test data points. The weight factors will evolve as ChemProps grows. With ChemProps, other polymer databases can remove duplicate entries and enable a more accurate “search by SMILES” function by using ChemProps as a common name-to-SMILES translator through API calls. ChemProps is also an excellent tool for auto-populating polymer properties thanks to its easy-to-update design.

scholarly journals Higher-order structure of polymer melt described by persistent homology

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yohei Shimizu ◽  
Takanori Kurokawa ◽  
Hirokazu Arai ◽  
Hitoshi Washizu
Keyword(s):  
Cell Size ◽  
Md Simulation ◽  
Persistent Homology ◽  
Polymer Melt ◽  
Higher Order ◽  
Polymer Materials ◽  
Order Structure ◽  
Materials Informatics ◽  
Optimal Method ◽  
Amorphous Nature

AbstractThe optimal method of the polymer Materials Informatics (MI) has not been developed because the amorphous nature of the higher-order structure affects these properties. We have now tried to develop the polymer MI’s descriptor of the higher-order structure using persistent homology as the topological method. We have experimentally studied the influence of the MD simulation cell size as the higher-order structure of the polymer on its electrical properties important for a soft material sensor or actuator device. The all-atom MD simulation of the polymer has been calculated and the obtained atomic coordinate has been analyzed by the persistent homology. The change in the higher-order structure by different cell size simulations affects the dielectric constant, although these changes are not described by a radial distribution function (RDF). On the other hand, using the 2nd order persistent diagram (PD), it was found that when the cell size is small, the island-shaped distribution become smoother as the cell size increased. There is the same tendency for the condition of change in the monomer ratio, the polymer chain length or temperature. As a result, the persistent homology may express the higher-order structure generated by the MD simulation as a descriptor of the polymer MI.

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